“Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

“NoTaMe”: Workflow for Non-Targeted LC-MS Metabolic Profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics, in order to provide coherent and high-quality data that enables discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce NoTaMe, an analytical workflow for non-targeted metabolic profiling approaches utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research, and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data, and finally to identify and interpret the compounds that have emerged as interesting.

Advances in Research on Diabetes by Human Nutriomics

The incidence and prevalence of diabetes mellitus (DM) have increased rapidly worldwide over the last two decades. Because the pathogenic factors of DM are heterogeneous, determining clinically effective treatments for DM patients is difficult. Applying various nutrient analyses has yielded new insight and potential treatments for DM patients. In this review, we summarized the omics analysis methods, including nutrigenomics, nutritional-metabolomics, and foodomics. The list of the new targets of SNPs, genes, proteins, and gut microbiota associated with DM has been obtained by the analysis of nutrigenomics and microbiomics within last few years, which provides a reference for the diagnosis of DM. The use of nutrient metabolomics analysis can obtain new targets of amino acids, lipids, and metal elements, which provides a reference for the treatment of DM. Foodomics analysis can provide targeted dietary strategies for DM patients. This review summarizes the DM-associated molecular biomarkers in current applied omics analyses and may provide guidance for diagnosing and treating DM.

Pre-Analytical and Within-Person Reproducibility of Nutritional Metabolomics over 2 Years in Elders at Risk for Dementia (P18-121-19)

Objectives Nutritional metabolomics to objectively assess dietary intake in aging permit the opportunity to circumvent measurement errors that accompany subjective means of dietary assessment. At the same time, they may offer insights into mechanisms of action and metabolic disturbances that are actionable targets for modulation through diet in hopes of disease prevention and treatment. However, prior to more broad deployment the pre-analytical and temporal variation over time should be documented in order to design and interpret epidemiological studies properly. We quantified and examined 155 nutrient biomarkers and metabolites selected for their potential relevance to dementia. Methods Blood samples from three time points, spanning a 2-year period, were obtained from older adults participating in the NIA-Layton Oregon Alzheimer's Disease Center's Nutrition and Brain Aging Study (NBAS). Blood samples were batched randomly across three time points for quantification of blood amino acids, minerals, water and fat-soluble micronutrients, lipids, one carbon, and kynurenine pathway metabolites using a variety of methods including, tandem mass spectrometry. Pre-analytical coefficients of variation (CV) were calculated for all the biomarkers and intraclass correlation coefficients (ICC) were calculated to evaluate the within-person reproducibility in a subset of 137 participants. Results The mean baseline age of the analytic sample (n = 137) was 85.6 (± 8.3, 57 - 101 years), 70% are female, 21% carry the ApoEe4 allele and MMSE was 28.3 (± 1.78). The pre-analytical CVs ranged from 0.9% to 55.0% and the ICC ranged from 0 to 0.87 (25%-tile/median/75%-tile 0.41/0.54/0.66). Twenty four % had ICC < 0.40, 66% had ICC 0.40 −0.75 and 10% had ICC > 0.75. Conclusions The pre-analytical and within-person reproducibility of nutritional metabolomics in aging ranges widely. The majority can reliably estimate average concentrations over a 2 year period from a single time point and the biomarkers with ICC's above 0.40 can be used for correction of measurement error and those below 0.40 should consider multiple samples per subject and exploring the methodological and biological explanation for the variation over time. Funding Sources Nestle Institute of Health Sciences, Hinda and Arthur Marcus Institute for Aging Research, NIA-Layton Aging & Alzheimer's Disease Center (P30AGO8017).

Nutritional Metabolomics: Postprandial Response of Meals Relating to Vegan, Lacto-Ovo Vegetarian, and Omnivore Diets

Metabolomics provide an unbiased tool for exploring the modulation of the human metabolome in response to food intake. This study applied metabolomics to capture the postprandial metabolic response to breakfast meals corresponding to vegan (VE), lacto ovo-vegetarian (LOV), and omnivore (OM) diets. In a cross over design 32 healthy volunteers (16 men and 16 females) consumed breakfast meals in a randomized order during three consecutive days. Fasting and 3 h postprandial serum samples were collected and then subjected to metabolite profiling using 1H-nuclear magnetic resonance (NMR) spectroscopy. Changes in concentration of identified and discriminating metabolites, between fasting and postprandial state, were compared across meals. Betaine, choline, and creatine displayed higher concentration in the OM breakfast, while 3-hydroxyisobutyrate, carnitine, proline, and tyrosine showed an increase for the LOV and unidentified free fatty acids displayed a higher concentration after the VE breakfast. Using 1H NMR metabolomics it was possible to detect and distinguish the metabolic response of three different breakfast meals corresponding to vegan, lacto-ovo vegetarian, and omnivore diets in serum.

Use of Metabolomics in Improving Assessment of Dietary Intake

BACKGROUND Nutritional metabolomics is rapidly evolving to integrate nutrition with complex metabolomics data to discover new biomarkers of nutritional exposure and status. CONTENT The purpose of this review is to provide a broad overview of the measurement techniques, study designs, and statistical approaches used in nutrition metabolomics, as well as to describe the current knowledge from epidemiologic studies identifying metabolite profiles associated with the intake of individual nutrients, foods, and dietary patterns. SUMMARY A wide range of technologies, databases, and computational tools are available to integrate nutritional metabolomics with dietary and phenotypic information. Biomarkers identified with the use of high-throughput metabolomics techniques include amino acids, acylcarnitines, carbohydrates, bile acids, purine and pyrimidine metabolites, and lipid classes. The most extensively studied food groups include fruits, vegetables, meat, fish, bread, whole grain cereals, nuts, wine, coffee, tea, cocoa, and chocolate. We identified 16 studies that evaluated metabolite signatures associated with dietary patterns. Dietary patterns examined included vegetarian and lactovegetarian diets, omnivorous diet, Western dietary patterns, prudent dietary patterns, Nordic diet, and Mediterranean diet. Although many metabolite biomarkers of individual foods and dietary patterns have been identified, those biomarkers may not be sensitive or specific to dietary intakes. Some biomarkers represent short-term intakes rather than long-term dietary habits. Nonetheless, nutritional metabolomics holds promise for the development of a robust and unbiased strategy for measuring diet. Still, this technology is intended to be complementary, rather than a replacement, to traditional well-validated dietary assessment methods such as food frequency questionnaires that can measure usual diet, the most relevant exposure in nutritional epidemiologic studies.